One of the problems encountered when displaying objects is the large quantity of data to be taken into account on each movement of the observation point, which might be moved very fast under the control of the user.
The paper by Peter Wonka, Michael Wimmer, and Dieter Schmalstieg, “Visibility Preprocessing with Occluder Fusion for Urban Walkthroughs”, Proceedings of Eurographic Rendering Workshop, 2000, proposes dividing the observation space into viewing cells, reducing occluder objects by an amount ε and, for each cell, determining a sufficient number of sampling points thereof and calculating the visibility for each sampling point to obtain a set of objects potentially visible from that cell.
Those sets of potentially visible objects are present for each cell, however, and represent a very large volume of data.
The paper by Michiel van de Panne and A. James Stewart, “Effective Compression Techniques for Precomputed Visibility”, Rendering Techniques 1999, pages 305 to 316, 1999 proposes dividing the observation point space into small regions or cells, and constructing a Boolean visibility table, coding information determined beforehand in a preliminary calculation step, and indicating which polygons are visible from each region. In that visibility table, each row corresponds to a cell of observation points and each column corresponds to a polygon. Any entry situated in row i, column j of the visibility table is in the True state if and only if the polygon j is at least potentially visible from a point of the region i. To compress the visibility table, a lossy compression algorithm merges rows and columns having similar sets of True entries. A different, this time lossless, compression algorithm adds new rows and columns to the visibility table, the new rows and columns being obtained from rows having common entries. Consequently, either information is lost in the first algorithm or the visibility table remains too large. Moreover, those algorithms are not suitable for networked display, i.e. for display on a user station remote from a database storing the visibility data.
The following documents may also be cited:                C. Gotsman, O. Sudarsky, and J. Fayman, “Optimized Occlusion Culling Using Five-Dimensional Subdivision”, Computer Graphics, 23(5): 645-654, 1999, which describes an occlusion sorting algorithm organizing space into a five-dimensional hierarchical visibility tree structure, in which each leaf of the tree contains a reference to one of the leaves of lower level. A True value for the ith position of a leaf indicates the presence of the ith object in that leaf, whereas a False value indicates that the object is not present in that leaf or in the lower level leaves. A drawback of that algorithm is that the tree must be traversed for each observation point. It is not suitable for networked display on a user station remote from a database storing the visibility data.        Boaz Nadler, Gadi Fibish, Shuly Lev-Yehudi, and Danile Cohen-Or, “A qualitative and quantitative visibility analysis in urban scenes”, Computer & Graphics, 23(5): 655-666, 1999, which calculates the probability that a given object is visible from a given viewing cell as a function of the distance relative to that cell. The intersection of the sets of potentially visible objects for neighboring cells is estimated in the cell, and cell to cell consistency is exploited to store the data in a hierarchical structure to reduce the number of duplications. The quantity of data nevertheless remains too large.        Christopher Zach and Konrad Karner, “Progressive compression of visibility data for view-dependent multiresolution meshes”, Journal of WSCG, vol. 11, no. 3, pp. 546-553, 2003, which proposes a compression method in which visibility information can be stored directly in nodes of a multiresolution structure, and only the necessary portions of the visibility data are transmitted. That method does not determine all of the objects that are visible each time, because it is necessary to await the arrival of new data, which was not transmitted the first time, which introduces unwanted latency into the method.        V. Koltun, Y. Chrysanthou, and D. Cohen-Or, “Virtual Occluders: An Efficient Intermediate PVS Representation”, Eurographics Workshop on Rendering, pages 59-70, Eurographics, 2000, which limits itself to defining virtual occluders for a viewing cell, in order to construct a potentially visible set (PVS) for that cell using those virtual occluders, but does not address the problem of the large quantity of data obtained.        
Fabio O. Moreira, Joao L. D. Comba, and Carla M. D. S. Freitas, “Smart Visible Sets for Networked Virtual Environments”, SIBGRAPI 2002, which defines smart visible sets (SVS) that correspond to a partitioning of the information of the potentially visible sets (PVS) into dynamic subsets taking account of position. A classification mechanism enables only data classified as being the most important to be transmitted. Consequently, there is a loss of data. That technique has not been tested for very large databases, or in a network situation.